Organic aerosols are measured at a remote site (Ersa) on the cape of Corsica
in the northwestern Mediterranean basin during the winter campaign of 2014 of the CHemistry and AeRosols Mediterranean
EXperiment (CharMEx), when high organic
concentrations from anthropogenic origins are observed.
This work aims to represent the observed organic aerosol
concentrations and properties (oxidation state) using the
air-quality model Polyphemus with a surrogate approach for secondary organic
aerosol (SOA) formation.
Because intermediate and semi-volatile organic compounds (I/S-VOCs) are the main
precursors of SOAs at Ersa during winter 2014, different parameterizations
to represent the emission and aging of I/S-VOCs were implemented in the
chemistry-transport model of Polyphemus (different volatility distribution
emissions and single-step oxidation vs multi-step oxidation within a volatility basis set – VBS – framework, inclusion of non-traditional volatile organic
compounds – NTVOCs). Simulations using the different parameterizations are compared to
each other and to the measurements (concentration and oxidation state). The
highly observed organic concentrations are well reproduced in all the
parameterizations. They are slightly underestimated in most
parameterizations. The volatility distribution at emissions influences the concentrations more strongly than the choice of the parameterization that may
be used for aging (single-step oxidation vs multi-step oxidation), stressing
the importance of an accurate characterization of emissions. Assuming the
volatility distribution of sectors other than residential heating to be the
same as residential heating may lead to a strong underestimation of organic
concentrations. The observed organic oxidation and oxygenation states are
strongly underestimated in all simulations, even when multigenerational
aging of I/S-VOCs from all sectors is modeled. This suggests that
uncertainties in the emissions and aging of I/S-VOC emissions remain to be
elucidated, with a potential role of formation of organic nitrate and
low-volatility highly oxygenated organic molecules.

OAs originate not only from the partitioning of POAs between the gas and the
particle phases but also from secondary organic aerosol (SOA) formation through the
gas-to-particle partitioning of the oxidation products of biogenic and
anthropogenic volatile organic compounds (VOCs) and intermediate and semi
volatile organic compounds (I/S-VOCs). The main biogenic VOC precursors are
terpenes (α-pinene, β-pinene, limonene, humulene) and isoprene
(Shrivastava et al., 2017), while the main anthropogenic VOC precursors are
aromatics (Dawson et al., 2016; Gentner et al., 2017).

Available measurements and modeling studies are useful in elucidating the
composition and origin of OAs in different seasons
(Canonaco et al., 2015; Chrit et al., 2017; Ciarelli et al., 2017b; Couvidat et al., 2012; Hayes et al., 2015).
Indeed, over the Mediterranean region, the oxidation of biogenic VOCs may
dominate the formation of OAs during the summer
(Chrit et al., 2017; El Haddad et al., 2013; Minguillón et al., 2016). Chrit et al. (2017) found that
I/S-VOC emissions do not influence the concentrations of OAs in summer
over the Mediterranean region much, but biogenic SOAs prevail. Because biogenic
emissions are low in winter, Canonaco et al. (2015) demonstrated a clear shift
in the SOA origin between summer and winter during a measurement campaign
from February 2012 to February 2013 conducted in Zurich using the Aerosol
Chemical Speciation Monitor (ACSM; Ng et al., 2011) measurements. This last
study notably highlights the importance of biogenic VOC emissions and
biogenic SOA production in summer as well as the importance of residential heating
in winter. Ciarelli et al. (2017a) performed a source apportionment study at
the European scale and revealed that residential combustion (mainly related
to wood burning) contributed about 60 %–70 % to SOA formation during
the winter, whereas non-residential combustion and road-transportation sector
contributed about 30 %–40 % to SOA formation. Moreover, residential
heating can also be a source of POAs, which may make up a large fraction
(20 % to 90 %) of the submicron particulate matter in winter
(Denier van der Gon et al., 2015b; May et al., 2013a; Murphy et al., 2006; Shrivastava et al., 2017).

Modeling OA concentrations in winter is challenging, because it mostly involves
the characterization of I/S-VOC emissions and aging. Standard gridded
emission inventories, such as those of the European Monitoring and Evaluation
Programme (EMEP, http://www.emep.int, last access: 10 December 2018) over Europe, do not yet include I/S-VOC
emissions, and their emissions are still highly uncertain. For example,
Denier van der Gon et al. (2015a) estimated that emissions from residential wood combustion
were underestimated by a factor of 2–3 in the 2005 EUCAARI inventory. As an
indirect method of accounting for the missing organic emissions in the absence
of precise emission inventories, numerous modeling studies estimate the
I/S-VOC emissions from POA emissions
(Bergström et al., 2012; Ciarelli et al., 2017a; Couvidat et al., 2012; Koo et al., 2014; Zhu et al., 2016) or more
recently from VOC emissions (Murphy et al., 2017; Ots et al., 2016; Zhao et al., 2015, 2016). A
ratio of I/S-VOC / POA of 1.5 has been used in several air-quality studies (Bergström et al., 2012; Ciarelli et al., 2017a; Koo et al., 2014; Zhu et al., 2016). For
example, Zhu et al. (2016) simulated the particle composition over greater
Paris during the winter MEGAPOLI campaign, and they found that simulated OAs
agreed well with observed OAs when gas-phase I/S-VOCs emissions are estimated
using a ratio I/S-VOC / POA of 1.5, as derived following the
measurements at the tailpipe of vehicles representative of the average French
vehicle (Kim et al., 2016). However, various ratios are used to better fit the
measurements. For example, over Europe, Couvidat et al. (2012) used a I/S-VOC / POA ratio
of 4, but they also used a ratio of 6 in a sensitivity simulation
to better fit the observed OA concentrations in winter. Koo et al. (2014) used
an IVOC ∕ POA ratio of 1.5, but they also used a ratio of 3 in their high-IVOC emission
scenario.

The atmospheric evolution (also known as aging) of I/S-VOCs as well as their
impacts on atmospheric OA concentrations remain poorly characterized
(Murphy et al., 2006) and deserve a better understanding. A widely used approach
to model the aging of I/S-VOCs in CTMs is the volatility basis set (VBS)
approach (Donahue et al., 2006). I/S-VOCs are divided into several classes of
volatility where each class is represented by a surrogate. When oxidized by
the hydroxyl radical, it leads to the formation of surrogates of lower
volatility classes. This approach tends to lead to an overestimation of
simulated organic concentrations (Cholakian et al., 2018) if fragmentation is
not considered (formation of high-volatility surrogates during the
oxidation). Although the one-dimensional basis set (1-D VBS) accounts for the
volatility of the surrogates, it does not allow the representation of varying
oxidation levels of OAs. The prognostic tool to date that is more powerful, the two-dimensional
VBS approach (2-D VBS), although it is computationally
burdensome, describes not only the aging of I/S-VOCs using not only the volatility
property (C*) but also the oxidation level (the oxygen-to-carbon ratio
O : C), taking into account three competing processes: functionalization,
oligomerization and fragmentation (Donahue et al., 2012). Koo et al. (2014)
developed a 1.5-D aging VBS-type scheme that accounts for multigenerational
aging, including functionalization, oligomerization and fragmentation, and
that represents both the volatility and the oxidation properties of the
surrogates. When oxidized by a hydroxyl radical, each surrogate leads to the
formation of more-oxidized and less-volatile surrogates with a reduced carbon
number. Functionalization and fragmentation are implicitly taken into account
in this approach because of the increase of the oxygen number and the
decrease of the carbon number of the surrogates formed. The 1.5-D VBS module
is implemented within two widely used CTMs, namely the Comprehensive Air Quality Model
with extensions (ENVIRON, 2011)
and the Community Multiscale Air Quality Modeling System (Byun and Ching, 1999). Couvidat et al. (2012, 2013b, 2018) and Zhu et al. (2016) used a simplified aging scheme with three
volatility bins. When oxidized by the hydroxyl radical, each surrogate forms
a less-volatile and more-oxidized surrogate that does not undergo
multigenerational aging. This simplified aging scheme is implemented in the
two widely used CTMs, Polyphemus (Chrit et al., 2017) and Chimere
(Couvidat et al., 2018).

In winter, when anthropogenic emissions impact air quality the most,
anthropogenic emissions such as toluene and xylene may also form SOAs,
although they may be much less efficient than I/S-VOCs
(Couvidat et al., 2013a; Sartelet et al., 2018). To take into account the emissions and
aging of anthropogenic VOCs that are usually not considered in CTMs (phenol;
naphthalene; m-,o-,p-cresol; etc., Ciarelli et al. (2017b) modified the
approach of Koo et al. (2014) by considering non-traditional VOCs (NTVOCs).
They are VOCs or IVOCs, not usually taken into account in CTMs, with a
saturation concentration in the low range of IVOCs.

The oxidation level of OAs is important, because it is indicative of the degree
of hygroscopicity, surface tension (Jimenez et al., 2009), and radiative property
of the OAs in addition to its ability to act as cloud condensation nuclei (CCN)
over the Mediterranean (Duplissy et al., 2011; Jimenez et al., 2009; Wong et al., 2011).
Chrit et al. (2017) showed that, in summer in the western Mediterranean region,
OAs are highly oxidized and oxygenated. The CTM Polyphemus and Polair3d used in their study
does represent this high oxidation level of OAs after adding the
formation processes of highly oxidized species (autoxidation) and organic
nitrate formation to the model.

The particle oxidation state is represented by the organic-mass-to-organic-carbon ratio (OM : OC). According to Gilardoni et al. (2009) and
Kroll et al. (2011), OM : OC is an index of the contribution of heteroatoms
(O, H, S, N, etc.) to the organic mass; chemically processed and aged
particles are expected to have higher OM : OC ratios compared to freshly
emitted and unprocessed aerosols. The oxygenation state is represented by the
oxygen-to-carbon ratio (O : C). It indicates the contribution of oxygen to
organic molecules and the ability of carbon atoms to form bonds with oxygen.

Although the organic matter to organic carbon ratio (OM : OC) was first
believed to lie between 1.2 and 1.4 (Grosjean and Friedlander, 1975), numerous studies
(Aiken et al., 2008; Canagaratna et al., 2015; Couvidat et al., 2012; El-Zanan et al., 2005; Tost and Pringle, 2012; Tsimpidi et al., 2018; Turpin and Lim, 2001) show that OM : OC is approximately 1.6 for
urban aerosols and 2.1 for non urban aerosols. Zhang et al. (2005a) developed an
algorithm to deconvolve the mass spectra of OAs obtained with an
Aerodyne™ aerosol mass spectrometer (AMS) in
order to estimate the mass concentrations of hydrocarbon-like and oxygenated
organic aerosols (HOAs and OOAs). The mass of HOAs represents primary sources,
with an OM : OC ratio close to 1.2 and O : C ratio close to 0.1, while the
mass of OOAs represents secondary sources (aged and oxygenated) with an
OM : OC ratio close to 2.2 and an O : C ratio close to 1 (Aiken et al., 2008).
Using this technique, Zhang et al. (2005b) found an average OM : OC ratio of
1.8 in Pittsburgh in September. Over Europe, Crippa et al. (2014) found that
secondary OAs are dominant in the OA fraction, with primary sources
contributing to less than 30 % of the total mass fraction.
Xing et al. (2013) measured a ratio OM : OC ratio over 14 cities throughout
China and found that in summer, OM : OC is nearly 1.75 ± 0.13, while
the ratio is lower in winter (1.59 ± 0.18). The OM : OC ratio is lower
during winter due to the slow oxidation process owing to the low temperatures
in addition the low biogenic contribution to OA mass during winter. At Ersa,
over the Mediterranean and during the summer, Chrit et al. (2017) found high
OM : OC and O : C ratios (2.5 and 1 respectively). They are due to aged
biogenic OAs, which Chrit et al. (2017) were able to represent by adding the
formation of extremely low-volatility species and organic nitrate to the
model and by considering the formation of organosulfate.

Quantifying the effect of I/S-VOC emissions and their impact on the
atmospheric organic budget as well as the OA oxidation and oxygenation levels
during different seasons is challenging in spite of the recent advances
concerning the description of I/S-VOCs (Ciarelli et al., 2017b; Stockwell et al., 2015).
This work aims to evaluate how commonly used parameterizations and
assumptions of I/S-VOCs emissions and aging perform to model the OA
concentrations and properties in the western Mediterranean region in winter.
To that end, the CTM from the air-quality platform Polyphemus is used with
different parameterizations of I/S-VOCs emissions and aging.

This paper is structured as follows. Section 2 presents the setup of the
air-quality model used and reference measurements. Section 3 presents the
different emissions and aging mechanisms used to describe the evolution of
I/S-VOCs as well as the comparison method. Section 4 compares the simulated
concentrations, which are compositions of OAs for the simulations using the different
parameterizations. Finally, Sect. 5 compares the measured and simulated
OM : OC and O : C ratios.

The period of interest of this study is January–March 2014, hereafter
referred to as the winter 2014 campaign.

2.1 General model setup

The Polyphemus and Polair3d air-quality model is used, with a similar setup to
Chrit et al. (2017). Transport and dry and wet deposition are modeled following
Sartelet et al. (2007). The Carbon Bond 05 model is used for gas-phase
chemistry. Semi-volatile organic compound formation mechanisms from five SOA
gaseous precursors, namely isoprene, monoterpenes, sesquiterpenes, and aromatic
compounds, and intermediate and semi-volatile organic compounds from
anthropogenic emissions (Couvidat et al., 2012; Kim et al., 2011), are added to CB05
model. These five precursors are modeled with a few surrogates as proxies to
represent all the species. The aerosol dynamics (coagulation and
condensation and evaporation) are modeled using the size-resolved aerosol model
(Debry et al., 2007) based on a sectional approach with an
aerosol distribution of 24 sections of bound diameters: 0.01, 0.0141, 0.0199,
0.0281, 0.0398, 0.0562, 0.0794, 0.1121, 0.1585, 0.199, 0.25, 0.316, 0.4, 0.5,
0.63, 0.79, 1.0, 1.2589, 1.5849, 1.9953, 2.5119, 3.5481, 5.0119, 7.0795 and
10.0 µm.

The thermodynamic model used for the condensation and evaporation of inorganic
aerosol is ISORROPIA v1 (Nenes et al., 1998), and the gas-particle partitioning of
SOAs is computed with SOAP (Couvidat and Sartelet, 2015). In order to compute the
gas-particle partitioning of both inorganics and organics, a bulk equilibrium
approach is adopted. After condensation and evaporation, the mass is
redistributed among size bins using the moving diameter algorithm
(Jacobson, 1997).

The simulations are run between 1 January and 2 April 2014 for both the
nesting (Europe) and the nested (Mediterranean) domains. The simulation
domains (Europe and Mediterranean) and the spatial resolution used in the
present study are the same as the ones used in Chrit et al. (2017). The
spatial resolutions used for the European and Mediterranean domains are
0.5∘×0.5∘ and 0.125∘×0.125∘
along longitude and latitude. 14 vertical levels are used for both domains
from the ground to 12 km. The heights of the cell interfaces are 0, 30, 60,
100, 150, 200, 300, 500, 750, 1000, 1500, 2400, 3500, 6000 and 12 000 m.

Boundary conditions for the European domain are obtained from the global
chemistry-transport model MOZART v4.0
(Horowitz et al., 2003; https://www.acom.ucar.edu/wrf-chem/mozart.shtml,
last access: 10 December 2018). The European simulation provides initial and
boundary conditions to the Mediterranean one. The European Centre for
Medium-Range Weather Forecasts (ECMWF) model provides the meteorological
fields. The Troen and Mahrt parameterization (Troen and Mahrt, 1986) is used to
compute the vertical diffusion. The land cover is modeled using the Global
Land Cover 2000 (GLC-2000;
http://forobs.jrc.ec.europa.eu/products/glc2000/data_access.php, last
access: 10 December 2018) data set. Sea-salt emissions are parameterized
following Jaeglé et al. (2011) and are assumed to be composed of sodium,
chloride and sulfate (Schwier et al., 2015). Biogenic emissions are estimated
with the Model of Emissions of Gases and Aerosols from Nature (MEGAN,
Guenther et al., 2006). Anthropogenic emissions are generated using the
EDGAR-HTAP_V2 inventory for 2010
(http://edgar.jrc.ec.europa.eu/htap_v2/, last access:
10 December 2018). The monthly and daily temporal distribution for the
different activity sectors are obtained from GENEMIS (1994), and the hourly
temporal distribution is obtained from Sartelet et al. (2012). NOx,
SOx and PM2.5 emissions are speciated as described
in Chrit et al. (2017). I/S-VOC gas-phase emissions are estimated from the POA
emissions from residential heating by multiplying them by a constant factor
assumed to be 1.5 in the default simulation. The total (gas and particle) I/S-VOC is therefore
equal to 2.5 times the original POA.

As described in Sect. 3.5, different values will be used and compared
for I/S-VOC gas-phase emissions from residential heating and from other
sectors. The I/S-VOCs emissions from residential heating are assumed to be
those of the sector “htap_6_residential” of the EDGAR-HTAP_V2 inventory.
The emissions from this sector (shown in Fig. 1) concern the
emissions from heating and cooling and equipment and lighting of buildings as well
as waste treatment. The I/S-VOC emissions from residential heating (RH) are
obtained from the POA emissions of sector 6 by multiplying them by a constant
factor represented by RRH= I/S-VOC / POA. These emissions over the
Mediterranean domain are located over big cities (Marseille, Milan, Rome,
etc.). I/S-VOC emissions from the six other anthropogenic sources (shown in
Fig. 1) are estimated from the POA emissions by multiplying
them by a constant factor noted R= I/S-VOC / POA. These emissions are
located over big cities and along the main traffic routes as well as on the
shipping routes linking Marseille to Ajaccio and Bastia. Different
estimations of R and RRH will be used as well as different
approaches to represent the aging of I/S-VOCs (Sect. 3).

Figure 1Surface emissions of POAs from the residential
heating sector (a) and from the other six anthropogenic
sectors (b) during winter 2014. The emissions are in
µg m−2 s−1.

2.2 Measurement setup

The ground-based measurements were performed in the framework of the
CHemistry and AeRosols Mediterranean EXperiment (ChArMEx) at Ersa
(42∘58′ N, 9∘21.8′ E) on a ridge at the northern tip of
the island of Corsica, at an altitude of about 530 m a.s.l. The
ground-based comparisons are performed by comparing the measured and modeled
concentrations at the model cell closest to the station (42∘52 N,
9∘22′30′′ E, 494 m a.s.l.), as detailed in Chrit et al. (2017).
An Aerodyne™ ACSM was used in order to measure
the near real-time mass concentration and chemical composition of aerosols
with aerodynamic diameters between 70 and 1000 nm, with a time resolution of
30 min (Ng et al., 2011). This instrument has been continuously running at Ersa
between June 2012 and July 2014 (Nicolas, 2013), with an on-site setup
similar to that presented in Michoud et al. (2017). A recent intercomparison
exercise, in which the ACSM used in this study has successfully taken part,
reports an expanded uncertainty of 19 % for OM (Crenn et al., 2015).
OM : OC and O : C ratios are estimated using these measurements,
following the methodology provided in Kroll et al. (2011).
Although Crenn et al. (2015) and Fröhlich et al. (2015) have shown consistent
results (e.g., satisfactorily Z-scores) in terms of fragmentation pattern,
higher discrepancies were observed for f44 (mass fraction of m∕z 44),
which is an essential variable in the calculation of these elemental ratios.
In this respect, results are presented with an uncertainty which can be
estimated as being twice the one of PM (i.e., around 40 %). The
measurements are compared to concentrations and properties of particles of
diameters between 0.01 and 1 µm.

2.3 Model and measurement comparison method

To evaluate the performance of the model, we compare model simulation results
to measurements at the Ersa site using a variety of performance statistical
indicators. These indicators are: the simulated mean (s‾), the
root-mean-square error (RMSE), the correlation coefficient (corr), the mean
fractional bias (MFB) and the mean fractional error (MFE). Table A1
of Appendix A lists the key statistical indicators definitions used
in the model-to-data intercomparison. Furthermore, the criteria of
Boylan and Russell (2006), which is detailed in Table A2 of
Appendix A, is used to assess the performance of the simulations.

In order to understand the behavior of the different parameterizations
commonly used in CTMs to represent emissions and aging of I/S-VOCs in the
western Mediterranean region, several simulations using different
parameterizations, described in the following sections, are compared. These
parameterizations are those described in Couvidat et al. (2012),
Koo et al. (2014) and Ciarelli et al. (2017b). The differences concern the
emission ratios used to estimate I/S-VOCs from POAs (R and RRH),
the aging scheme (one step or multi-generational), the modeling of NTVOCs
and the ratio OM : OC and volatility distribution at emissions.

3.1 One-step oxidation scheme

The one-step oxidation mechanism of Couvidat et al. (2012) is based on the
fitting of the curve of dilution of POAs from diesel exhaust of
Robinson et al. (2007). I/S-VOCs are modeled with three surrogate species
POAlP, POAmP and POAhP of different volatilities chosen to fit the dilution
curve of POAs from diesel exhaust of Robinson et al. (2007) and characterized by
their saturation concentrations (0.91, 86.21 and
3225.80 µg m−3, respectively).

The properties of the primary and aged I/S-VOCs are shown in
Table B1 of Appendix B. The
aging of each of these primary surrogates is modeled by a one-step
OH-oxidation reaction in the gas phase (Appendix B),
leading to the formation of secondary surrogates SOAlP, SOAmP and SOAhP. Once
formed, these secondary surrogates do not undergo further oxidations.
Compared to the primary surrogates, the volatility of the secondary
surrogates is reduced by a factor of 100, and their molecular weight is
increased by 40 % (Couvidat et al., 2012; Grieshop et al., 2009) to represent
functionalization and fragmentation.

3.2 Multi-generational step oxidation scheme

In sensitivity simulations, for anthropogenic I/S-VOC emissions, the
oxidation mechanism is based on the hybrid volatility basis set (1.5-D VBS)
approach developed by Koo et al. (2014). This mechanism combines the simplicity
of the 1-D VBS with the ability to describe evolution of OAs
in the 2-D space of oxidation state and volatility. This basis set
uses five volatility surrogates, characterized by saturation concentrations
varying between 0.1 and 1000 µg m−3. The surrogates VAP0,
VAP1, VAP2, VAP3 and VAP4 refer to the primary surrogates, and VAS0, VAS1,
VAS2, VAS3 and VAS4 refer to the secondary ones. Table C1 of
Appendix C lists their properties.

In the scheme developed by Koo et al. (2014), the OH-oxidation of the primary
surrogates leads to a mixture of primary and secondary surrogates of lower
volatility. The carbon number (and oxygen number for secondary surrogates) of
the lower volatility surrogate decreases (and increases for secondary
surrogates), indicating that functionalization and fragmentation are
implicitly accounted for. This mechanism is detailed in
Appendix C.

3.3 Multi-generational step oxidation scheme for residential heating

In sensitivity simulations, for anthropogenic I/S-VOC emissions from
residential heating, the VBS model developed by Ciarelli et al. (2017b) is also
used. As in the previously detailed multi-step oxidation scheme, five
surrogates with volatilities characterized by saturation concentrations
extending from 0.1 to 1000 µg m−3 are used. The primary
surrogates (BBPOA1, BBPOA2, BBPOA3, BBPOA4, BBPOA5) react with OH to form
secondary surrogates (BBSOA0, BBSOA1, BBSOA2, BBSOA3, BBSOA4), whose
volatility is 1 order of magnitude lower than the primary surrogate. In
opposition to the one-step and multi-step oxidation schemes detailed above,
here the secondary surrogates may also undergo OH-oxidation forming the
secondary surrogate of lower volatility. As in the other schemes,
functionalization and fragmentation are taken into account as the carbon and
oxygen numbers of the secondary surrogates decreases and increases
respectively. The properties of the VBS surrogates are shown in
Table D1 of Appendix D, where
reactions are also detailed.

Data from recent wood combustion and aging experiments performed in smog
chamber by Ciarelli et al. (2017b) show significant contribution of SOAs from
non-traditional volatile organic compounds (NTVOCs: phenol; m-, o-,
p-cresol; m-, o-, p-benzenediol/2-methylfuraldehyde;
dimethylphenols; guaiacol/methylbenzenediols; naphthalene;
2-methylnaphthalene/1-methylnaphthalene; acenaphthylene; syringol;
biphenyl/acenaphthene; and dimethylnaphthalene) to OA mass. These NTVOCs are
usually not accounted for as SOA precursors in CTMs. The NTVOC mixture saturation
concentration is estimated to be ∼106µg m−3,
falling with the IVOC saturation concentration range limit (Donahue et al., 2012; Koo et al., 2014). NTVOCs emissions are estimated using a ratio of
NTVOC ∕ SVOC of 4.75 (Ciarelli et al., 2017b) and their OH-oxidation
produces four secondary surrogates of different volatilities. These four
surrogates may undergo OH-oxidation leading to the less-volatile and more-oxidized secondary surrogate, similarly to the multi-step oxidation described
in Sect. 3.3. This mechanism is detailed in
Appendix D, and the surrogates properties are listed
in Table D1 of Appendix D.

3.4 Volatility distribution and properties of primary emissions

Table 1 shows emission rates of OA precursors averaged
over the Mediterranean domain and over the simulation period.

Table 1Emission rates of OA precursors averaged temporally and over the
Mediterranean domain.

Emissions of I/S-VOCs are allocated into the surrogate compounds detailed in
the above sections using emission distribution profiles, which are based on
chamber measurements. The distribution of the emission profiles as a function
of volatility (saturation concentration) is detailed in
Table 2. Two emission profiles are used. The first one
corresponds to the measurements of May et al. (2013b) for biomass burning, and
it is similar to the emission profile used by Couvidat et al. (2012) for all
sectors and by Ciarelli et al. (2017b) for residential heating. The second
emission profile corresponds to an average of emission distributions from
gasoline and diesel vehicles measured by May et al. (2013c, d), and it
is used in Koo et al. (2014). Here, the volatility emission distributions are
assigned to a profile number (equal to 1 or 2), depending on whether the
volatility profile is similar to the profile from biomass burning emissions
of May et al. (2013c) (profile number 2) or whether it is similar to the
profile from the vehicle emissions of May et al. (2013b, d)
(profile number 1). As shown in Table 2, the emitted
I/S-VOCs are less volatile in the profile 1 than in the biomass-burning
volatility distribution (profile 2). Depending on the emission sector, the
OM : OC and O : C ratios of the emitted surrogates may differ. For most
sectors, such as traffic, the OM : OC and O : C ratios are assumed to be
low; OM : OC is equal to 1.3 in Couvidat et al. (2012). However, for
residential heating, the emissions may be more oxidized. The scheme of
Ciarelli et al. (2017b) assumes higher OM : OC and O : C rations, as
described in Table 3. Here, the OM : OC and O : C ratios are
assigned to a profile number (equal to 1 or 2), depending on whether the
ratios are similar to the profile from the biomass burning emissions of
Ciarelli et al. (2017b)(profile number 2) or whether they are lower (profile
number 1).

Table 2Summary of the volatility distributions of the primary I/S-VOC
surrogates. Saturation concentrations are expressed in
µg m−3. For each saturation concentration and volatility
coefficient, the name of the associated primary surrogate is in
square brackets.

Table 3Summary of the OM : OC (and O : C) ratio of the primary I/S-VOC
surrogates. Saturation concentrations are expressed in
µg m−3. For each saturation concentration and OM : OC ratio,
the name of the associated primary surrogate is in square brackets.

3.5 Sensitivity simulations

The setup of the different simulations is summarized in
Table 4. The simulation S1 uses the setup commonly used in
air-quality simulations with the Polyphemus platform: the one-step aging
scheme of Couvidat et al. (2012) is used for both residential heating and other
anthropogenic sectors.

The links between the compared simulations and the sensitivity parameters
studied are summarized in Table 5. The simulation S2 is
conducted to evaluate the impact of the volatility distribution of emissions.
Instead of using a volatility distribution specific of biomass burning for
all sectors as in S1, the volatility distribution specific of car emissions
is used for anthropogenic sectors other than residential heating.

The simulation S3 is conducted to evaluate the impact of the aging scheme.
The volatility distributions are similar as S2, but multi-generational
schemes are used rather than a single-oxidation strep for all anthropogenic
sectors.

The simulation S4 is evaluated to estimate the impact of NTVOCs. It has the
same setup as S2 with multi-generational aging, but NTVOCs are taken into
account. Even though NTVOCs are added, emissions of I/S-VOCs as modeled by
the factor RRH are kept.

The simulations S5 and S6 are conducted to assess the impact of the
I/S-VOC / POA ratio used for residential heating
(RRH). The simulation S5 has the same setup as the simulation S2
(single-step oxidation), but it differs in the ratio RRH, which
is assumed to be equal to 4 rather than 1.5. The simulation S6 has the same
setup as the simulation S4 (multi-step oxidation and NTVOCs), but it differs
in the ratio RRH, which is assumed to be equal to 4 rather than
1.5.

In terms of the OM : OC ratio, the ratio specific of car emissions is used
for emissions from anthropogenic sectors other than residential heating. For
residential heating, higher OM : OC ratios are used in all simulations,
except in S1, where the ratio specific of car emissions is used for all
sectors.

Table 4Summary of the parameters used in the different simulations
performed.

The simulated composition of OM1 at Ersa is shown in
Fig. 2 for the simulations S4 and S5. In all
simulations, primary and secondary organic aerosols (POAs and SOAs) from
anthropogenic I/S-VOCs are the main components of the organic mass (between
60 % and 84 %). POAs tend to account for almost the same fraction of
the organic mass than SOAs (between 46 % and 62 %). Similarly, in the
U.S., Koo et al. (2014) found that the SOAs account for less than half of the
modeled OA mass in winter 2005 due to the slow chemical aging during the
cold season. Over Europe, in March 2009, Ciarelli et al. (2017a) simulated that
POAs account for between 12 % and 68 % of the OAs, with an average value
of 38 %. The emission sector 6 (residential heating) has a large
contribution to OAs (between 31 % and 33 %). This is also in line with
Ciarelli et al. (2017a) who found that, over Europe in March 2009, the
contribution of the residential sector to OAs varies between 20 % and
45 %, with an average value of 38 %. Furthermore, this sector
contributes more to SOAs (between 42 % and 52 % of SOAs from I/S-VOCs)
than to POAs (between 17 % and 31 % of POAs from I/S-VOCs), because
their I/S-VOC emissions are more volatile.

The contribution from aromatic VOCs is low (lower than 3 %), and when
NTVOCs are considered, they represent between 18 % and 21 % of the
organic mass. The model simulations performed revealed that, for winter
2014, the biogenic OA fraction is low (15 %–18 %).
Ciarelli et al. (2017a) also estimated the biogenic contribution to the organic
budget to be between 5 % and 20 % over Europe.

Figure 2Simulated composition of OM1 during the winter campaign
of 2014 for two simulations: S4 (a) and S5 (b).

The statistical evaluation of the simulations is shown in
Table 6. The model-to-measurement correlation is high for all
simulations (between 76 % and 83 %). The performance criterion is
satisfied for all simulations, and the goal criterion is satisfied for S2, S3,
S4 and S5. The goal criterion is not satisfied for the simulation S1, which
uses single-step oxidation with a biomass-burning-type volatility
distribution for all anthropogenic sectors as well as for the simulation S6, which
uses multi-step oxidation with NTVOCs and a high RRH ratio. The
simulation S1 strongly underestimates the OM1 concentration at Ersa,
whereas the simulation S6 strongly overestimates it. All the simulations
tend to underestimate the OM1 concentrations at Ersa, except for the
two simulations where NTVOCs are taken into account (S4 and S6), which
overestimate the OM1 concentrations at Ersa. Because I/S-VOC
emissions as modeled by the factor RRH are kept in those
simulations, the IVOCs forming SOAs may have been counted twice by adding
NTVOCs, explaining the overestimation.

Other CTMs showed the same underestimation of OM1 concentrations
during winter over Europe, even when I/S-VOC emissions are taken into account
(Couvidat et al., 2012; Denier van der Gon et al., 2015a). The CTM CAMx also underestimated the organic
concentrations over Europe during February and March 2009
(Ciarelli et al., 2017a), but considerable improvement was found for the modeled
OA mass, with the MFB decreasing from −61 % to −29 % when the
parameterization of Ciarelli et al. (2017b) with NTVOCs was added.

Table 6Statistics of model to measurements comparisons for daily
OM1
concentrations during the winter campaign of 2014 at Ersa.
o‾ refers to the observed mean. Other statistical indicators
are defined in Table A1 of AppendixA.

The model-to-measurement comparison during the first 3 months of 2014 is shown in
Fig. 3 in terms of the daily concentrations of OM1 at Ersa. Globally, the temporal variations of the simulated
concentrations are well reproduced by the model. The simulation S1, which
uses single-step oxidation with a biomass-burning-type volatility
distribution for all anthropogenic sectors, underestimates the peaks.
However, the peaks are well reproduced by the simulations S2, S3 and S5. The
simulations S4 and S6, which take into account NTVOCs, overestimate the
peaks. All simulations underestimate the beginning of the peak between 9 and
15 March, probably due to uncertainties in meteorology, especially rain
episodes and changes in the origin of air mass.

Figure 3Daily evolution of measured and simulated OM1concentrations at
Ersa from 1 January to 2 April.

As detailed in Sect. 3.5, the differences between the simulations S2
and S1 originate in differences in the volatility distribution of emissions
from anthropogenic sectors other than residential heating. In the simulation
S2, a less-volatile distribution is used than in the simulation S1, leading
to larger OA concentrations in the particle phase. This difference in the
volatility distribution makes a large difference in the OA concentrations,
removing the strong underestimation simulated in simulation S1 (the MFB is
−55 % in S1 and only −23 % in S2).

Figure 4Daily evolution of the ratios OM : OC (a) and
O : C (b) from 1 January to 2 April 2014 at Ersa.

Considering multi-step aging for all anthropogenic sectors also leads to an
increase of OA concentrations (the MBF of the simulation S3 is −11 %,
which is lower in absolute value than the simulation S2). However, the
influence of the multi-step aging (difference between S2 and S3 shown in
Fig. E1 of Appendix E) is smaller than the
influence of the volatility distribution (difference between S1 and S2 shown
in Fig. E1 of Appendix E). This larger influence
of the volatility distribution than the multi-step aging is true not only at
Ersa but also over the whole Mediterranean domain, where the average RMSE
between the simulations S1 and S2 is 0.01 µg m−3 (impact of
volatility), compared to 0.005 µg m−3 for the RMSE between the simulations S2 and S3
(impact of multi-step aging).

At Ersa, increasing the ratio RRH from 1.5 to 4 (difference
between simulation S3 and S2 shown in Fig. E1 of
Appendix E) has almost the same impact as considering the
multi-step aging (difference between simulations S5 and S2 shown in Fig. E1 of Appendix E), although the
statistics are slightly better when the ratio RRH is increased
from 1.5 to 4 than when multi-step aging is considered. However, this is not
true over the whole Mediterranean domain, where the impact of increasing the
ratio RRH from 1.5 to 4 is large over cities, whereas the impact
of multi-step aging stays small (see Fig. E1 of
Appendix E). Over the whole Mediterranean domain, the
average RMSE between the simulations S2 and S5 is 0.014 µg m−3
(impact of increasing the ratio RRH from 1.5 to 4), compared to
0.005 µg m−3 for the RMSE between the simulations S2 and S3 (impact
of multi-step aging).

Finally, the best statistics in terms of MFE and MFB are obtained for the
simulation S5, with a one-step aging scheme, a volatility distribution
typical of biomass burning for the residential sector with a ratio
RRH of 4 and a volatility distribution typical of car emissions
for other sectors with a ratio R of 1.5.

The oxidation state is quantified using two metrics, OM : OC and O : C,
calculated as detailed in Chrit et al. (2017). Figure 4
shows the daily variations of the measured and simulated ratios for the
different simulations.

The measurements at Ersa show highly oxidized and oxygenated organics; the
measured OM : OC and O : C ratios at Ersa are 2.21±0.09 and 0.82±0.07, respectively. These values are lower than the index measured
during summer 2013 by Chrit et al. (2017) (2.43±0.07 and 0.99±0.06 for the measured OM : OC and O : C ratios at Ersa, respectively), due
to the slower oxidation process owing to the lower temperatures during
winter. The average simulated OM : OC and O : C ratios are shown in
Table 7. Both indices are strongly underestimated by all
simulations, due to the high contribution of POAs to the OM1
concentrations (POAs are less volatile and oxygenated than SOAs). The
simulations using multi-step aging schemes for I/S-VOC emissions have higher
OM : OC and O : C ratios, although the differences are very low; the
OM : OC ratio is 1.69±0.53 in S2 (single-step) and 1.72±0.50 in
S3 (multi-step). Organics in the simulations where the strength of I/S-VOC
emissions from residential heating was increased (simulations S5 and S6) and have
higher OM : OC and O : C ratios, because POAs and SOAs from I/S-VOCs from
residential heating are more oxidized and oxygenated than POAs and SOAs from
other anthropogenic sources. Similarly, organics in the simulations where
NTVOCs are taken into account have higher OM : OC and O : C ratios,
because in the model, NTVOCs lead to very oxidized and oxygenated OAs.
However, the simulated ratios OM : OC and O : C stay underestimated
(1.85 ± 0.38 and 0.60±0.24 at most, compared to 2.21±0.09 and
0.82±0.07 in the measurements).

Table 7Daily averages of OM : OC and O : C ratios at Ersa during winter
2014 for the different simulations. The average measured OM : OC ratio is
2.21 and the average measured O : C ratio is 0.82.

The underestimation of the O : C ratio may be due to an underestimation of
oxidants' concentrations and secondary aerosol formation. Figure 5 shows that the model tends to underestimate ozone concentrations
(the modeled and measured average concentrations between 21 January and 24 February 2014 are 46.2 and 68.0 µg m−3). However, the O : C
ratio stays underestimated even during the days where ozone is well modeled.
It is difficult to come to a conclusion on the underestimation of oxidants, because
measurements were not performed for other oxidants than ozone, such as OH,
which probably has other sources than ozone photolysis in winter.

Figure 5Daily evolution of ozone concentrations from 21 January to 24 February 2014 at Ersa.

This study shows a ground-based comparison of both modeled organic
concentrations and properties to measurements performed at Ersa (the cape of Corsica, France) during winter 2014. This work aims to evaluate how
commonly used parameterizations and assumptions of intermediate and semi-volatile
organic compound (I/S-VOC) emissions and aging perform in modeling OA
concentrations and properties in the western Mediterranean region in winter.
To that end, the chemistry-transport model from the air-quality platform
Polyphemus is used with different parameterizations of I/S-VOC emissions and
aging (different volatility distribution emissions, single-step oxidation vs
multi-step oxidation within a Volatility Basis Set framework, including
non-traditional volatile organic compounds NTVOCs). Winter 2014 simulations
are performed and compared to measurements obtained with an ACSM at the
background station of Ersa in the north of the island of Corsica. In all
simulations, OAs at Ersa are mainly from anthropogenic sources (only 15 % to
18 % of OAs are from biogenic sources). The emission sector 6 (residential
heating) has a large contribution to OAs (between 31 % and 33 %). The
contribution from aromatic VOCs is low (lower than 3 %). NTVOCs, as
modeled with the parameterization of Ciarelli et al. (2017b), represent between
18 % and 21 % of the organic mass. For most simulations, the
concentrations of OAs compare well to the measurements.

Over the whole western Mediterranean domain, the volatility distribution at
the emission influences the concentrations more strongly than the choice of
the parameterization that may be used for aging (single-step oxidation vs
multi-step oxidation). Modifying the volatility distribution of sectors other
than residential heating leads to a decrease of 29 % in OA concentrations
at Ersa, while using the multi-step oxidation parameterization rather than
the single-step one leads to an increase of 13 %. The best statistics are
obtained using two configurations; the first one is a one-step aging scheme,
a volatility distribution typical of biomass burning for the residential
sector with an I/S-VOC / POA ratio of 4 at emissions, and the second one is a multi-generational aging scheme,
a volatility distribution typical of car emissions for other sectors with a
R I/S-VOC / POA ratio of 1.5 at emissions.

Both the OM : OC and O : C ratios are underestimated at Ersa in all
simulations. The largest simulated OM : OC ratio is equal to 1.85±0.83, compared to 2.21±0.09 in the measurements. For the summer
campaign, Chrit et al. (2017) improved the simulated OM : OC ratio by adding
the formation mechanisms of organic
compounds with extremely low volatility from the autoxidation of monoterpenes
and organic nitrate from monoterpene oxidation. Similarly, the formation of
organic nitrate and highly oxygenated organic molecules (Molteni et al., 2018)
from the autoxidation of aromatic precursors should be added in order to
better reproduce the observed OA oxidation and oxygenation levels.

However, adding these new OA formation pathways may lead to an increase in OA
concentrations, suggesting that the actual parameterizations may need to be
revisited, for example by better characterizing their deposition. Because the
volatility distribution at the emission is the parameter influencing the
concentrations the most, further experimental research should therefore focus
on characterizing it for the different sectors. The emissions and formation
of compounds with very low volatility should also be further investigated to represent the aerosol
characteristics observed.

Table B1Properties of the primary and secondary anthropogenic I/S-VOCs. The
molecular weights are in g mol−1. ΔHvap is the enthalpy of
vaporization in KJ mol−1, which describes the temperature dependance of
the saturation pressure C*.

Table C1Properties of the VBS species (the primary and secondary
anthropogenic SVOCs). The molecular weights are in g mol−1.
ΔHvap is the enthalpy of vaporization in KJ mol−1, which
describes the temperature dependance of
the saturation pressure C*.

Table D1Properties of the VBS species (the NTVOCs
and primary and secondary RH-I/S-VOCs). The molecular weights are in g mol−1.
ΔHvap is the enthalpy of vaporization in KJ mol−1,
which describes the temperature dependance of
the saturation pressure C*.

Figure E1Maps of the concentrations of OM1 (µg m−3) averaged
from January to March 2014 using S1 (a) and the absolute
difference of OM1 concentrations between S2 and S1 (b, impact of volatility), S3 and S2 (c, impact of
multi-step aging), and S5 and S2 (d, impact of increasing
RRH from 1.5 to 4).

This research was funded by the French National Research Agency (ANR)
projects SAF-MED (grant ANR-12-BS06-0013). It is part of the ChArMEx project
supported by ADEME, CNRS-INSU, CEA and Météo-France through the
multidisciplinary programme MISTRALS (Mediterranean Integrated STudies at
Regional And Local Scales). It contributes to ChArMEx work packages 1 and 2
on emissions and aerosol aging, respectively. The ACSM at Ersa was funded by
the CORSiCA project, which was funded by the Collectivité Territoriale de Corse
through the Fonds Européen de Développement Régional of the European
Operational Program 2007–2013 and the Contrat de Plan Etat-Région. Eric
Hamounou is acknowledged for his great help in setting up the Ersa station.
We thank François Gheusi for the measurements of ozone concentrations.
CEREA is a member of the Institut Pierre Simon Laplace (IPSL).